Novel approaches to mobile application update management strategies for banking services

Posted: Mar 21, 2019

• Maria Harris
• Maria Nguyen
• Maria Sanchez

Abstract

This research introduces a paradigm-shifting framework for mobile banking application update management that fundamentally reimagines traditional approaches through the integration of behavioral economics, quantum-inspired optimization, and federated learning principles. Unlike conventional update strategies that prioritize technical efficiency or user convenience in isolation, our methodology synthesizes these competing objectives through a multi-dimensional optimization model that accounts for security vulnerabilities, user behavioral patterns, regulatory compliance requirements, and operational constraints simultaneously. The core innovation lies in our application of quantum annealing concepts to solve the complex scheduling problem inherent in banking app updates, treating the update deployment as a quantum system seeking its lowest energy state. This approach enables the discovery of optimal update windows that traditional algorithms would overlook due to computational complexity. Additionally, we adapt federated learning principles to create a privacy-preserving feedback mechanism that allows banks to collaboratively improve their update strategies without sharing sensitive user data. Our experimental evaluation across simulated banking environments demonstrates a 47